1. Field of the Disclosure
This disclosure is directed to a system and process for ranking the quality and/or importance of content on social networks such as Twitter, and particularly to a system and process for ranking the quality and/or importance of content on social networks such as Twitter using sports ranking processes.
2. Related Art
A number of well-known entities rank content based on user web browsing activities on the World Wide Web. The subsequent rankings are then used to derive a ranking of a webpage content that then may be subsequently used. For example, when you submit a query to Google, the order of the webpages are based on the relevance of the page to your submitted text (for instance, a search on “wildcat” would return a very different set of webpages than a search on “calculus”) and the “importance” of the page. Google uses an algorithm called PageRank to derive a measure of importance or quality of a webpage. This algorithm is based on a model of web surfing.
The PageRank model is relatively simple, in a certain sense, although its scalability is one of its most notable features. First, it assumes that 85% of the time you will follow links on a webpage. In particular, you have an equally likely chance of following any link on a given webpage. The remaining 15% of the time you will teleport to any webpage again with equal likelihood. When this model surfer reaches a webpage with no outlinks, the surfer will teleport to any webpage with equal likelihood. This is an idealized form of web surfing but the success of Google reflects how the model yields insightful results.
More specifically, PageRank is a link analysis algorithm that assigns a numerical weighting to each element of a hyperlinked set of documents, such as the World Wide Web, with the purpose of “measuring” its relative importance within the set. The algorithm may be applied to any collection of entities with reciprocal quotations and references. The numerical weight that it assigns to any given element E is referred to as the PageRank of E and denoted by PR(E).
While ranking content of the World Wide Web using, for example, a PageRank model of surfing works, there are places where it does not appear to apply as accurately to a social network. Accordingly, there is a need for a way in which to more accurately rank the importance or quality of content in a social network and give more insightful and helpful results than prior art ranking approaches such as PageRank.